As is known in the art, stereo disparity estimation has been a classical and well-studied problem in computer vision, with applications in several domains including large-scale three-dimensional (3D) reconstruction, scene estimation and obstacle avoidance for autonomous driving and flight. State-of-the-art methods can be focused on improving the reconstruction quality on specific datasets, with the obvious trade-off of speed versus employing sophisticated and computationally expensive techniques to achieve a desired level of quality. Further, many of these methods achieve higher reconstruction quality at slow response times.